BACKGROUND: How we spend our time and what we eat have important implications for our health. Evidence suggests that health-equivalent behaviour change options which result in the same benefit are available within both time use (physical activities, sedentary behaviours and sleep) and diet (e.g., fruit and vegetables, snack foods). However, it is not yet known if health-equivalent choices exist across both time-use and diet behaviours. This study aimed to explore if a variety of different time-use and dietary profiles were associated with equivalent physical functioning score among adolescents. METHODS: This study used cross-sectional data from 2123 adolescent participants from the Longitudinal Study of Australian Children (LSAC) (mean age = 14.4 ± 0.5 years), including time-use diaries (min/day of sleep, self-care, screen time, quiet time, physical activity, school-related and domestic/social), diet questionnaires (serves/day of fruit and vegetables, discretionary (snack) foods and sugar-sweetened beverages) and a measure of physical functioning (PedsQL™ 4.0 physical functioning scale for teens). Multiple linear regression models were used to find the association of 24-h time-use composition (expressed as isometric log ratios) and dietary variables with physical functioning score. The models were used to estimate which time-use and diet profiles (within a feasible range from the sample average) were associated with equivalent physical functioning scores. Finally, an interactive app was developed to make the results accessible to end users. RESULTS: Within 30 min and 1.5 servings of the average adolescent's time-use and dietary behaviours, 45 equivalent options were associated with a ~ 0.2 SD improvement in physical functioning scale. All options associated with this improvement in physical function involved increasing physical activity and increasing fruit and vegetable intake, whilst also reducing discretionary food intake and sugar-sweetened beverages. Most behavioural options also increased sleep and reduced time spent in self-care, screen time and quiet time activities. CONCLUSIONS: There are a range of time-use and diet profiles that may result in equivalent benefits in physical functioning among adolescents. Communicating these options using decision tools such as interactive apps may allow for tailored interventions across both time use and diet which are based on an individual's needs, preferences and constraints.

Alliance for Research in Exercise Nutrition and Activity Allied Health and Human Performance University of South Australia City East Campus Frome Rd GPO Box 2471 Adelaide SA 5001 Australia

Centre for Adolescent Health Royal Children's Hospital and Murdoch Children's Research Institute The University of Melbourne Parkville VIC Australia

Department of Paediatrics The University of Melbourne VIC Parkville Australia

Department of Public Health and Nursing Norwegian University of Science and Technology Trondheim Norway

Early Start School of Health and Society Faculty of the Arts Social Sciences and Humanities University of Wollongong Wollongong NSW Australia

Faculty of Physical Culture Palacký University Olomouc Czech Republic

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Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15(1):69. PubMed PMC

Saunders TJ, Gray CE, Poitras VJ, Chaput JP, Janssen I, Katzmarzyk PT, et al. Combinations of physical activity, sedentary behaviour and sleep: relationships with health indicators in school-aged children and youth. Appl Physiol Nutr Metab = Physiologie appliquee, nutrition et metabolisme. 2016;41(6 Suppl 3):S283–93. PubMed

Chen X, Maguire B, Brodaty H, O’Leary F. Dietary patterns and cognitive health in older adults: a systematic review. J Alzheimer’s Dis. 2019;67(2):583–619. PubMed

Bujtor M, Turner AI, Torres SJ, Esteban-Gonzalo L, Pariante CM, Borsini A. Associations of dietary intake on biological markers of inflammation in children and adolescents: a systematic review. Nutrients. 2021;13(2):356. PubMed PMC

Kwasnicka D, Dombrowski SU, White M, Sniehotta F. Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories. Health Psychol Rev. 2016;10(3):277–96. PubMed PMC

Dumuid D, Stanford TE, Martin-Fernández J-A, Pedišić Ž, Maher CA, Lewis LK, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38. PubMed

Talarico R, Janssen I. Compositional associations of time spent in sleep, sedentary behavior and physical activity with obesity measures in children. Int J Obes. 2018;42(8):1508–14. PubMed

Ng E, Wake M, Olds T, Lycett K, Edwards B, Le H, et al. Equivalence curves for healthy lifestyle choices. Pediatrics. 2021. 10.1542/peds.2020-025395. PubMed

Chastin S, McGregor D, Palarea-Albaladejo J, Diaz KM, Hagströmer M, Hallal PC, et al. Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis. Br J Sports Med. 2021;55(22):1277–85. PubMed PMC

Simpson SJ, Le Couteur DG, Raubenheimer D. Putting the balance back in diet. Cell. 2015;161(1):18–23. PubMed

Solon-Biet SM, McMahon AC, Ballard JW, Ruohonen K, Wu LE, Cogger VC, et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell Metab. 2014;19(3):418–30. PubMed PMC

Lee KP, Simpson SJ, Clissold FJ, Brooks R, Ballard JW, Taylor PW, et al. Lifespan and reproduction in Drosophila: new insights from nutritional geometry. Proc Natl Acad Sci USA. 2008;105(7):2498–503. PubMed PMC

Montiel-Rojas D, Santoro A, Nilsson A, Franceschi C, Capri M, Bazzocchi A, et al. Beneficial role of replacing dietary saturated fatty acids with polyunsaturated fatty acids in the prevention of sarcopenia: findings from the NU-AGE cohort. Nutrients. 2020;12(10):3079. PubMed PMC

Melaku YA, Reynolds AC, Gill TK, Appleton S, Adams R. Association between macronutrient intake and excessive daytime sleepiness: an iso-caloric substitution analysis from the North West Adelaide health study. Nutrients. 2019;11(10):2374. PubMed PMC

Koehler K, Drenowatz C. Integrated role of nutrition and physical activity for lifelong health. Nutrients. 2019;11(7):1437. PubMed PMC

Dumuid D, Olds T, Lange K, Edwards B, Lycett K, Burgner DP, et al. Goldilocks days: optimising children’s time use for health and well-being. J Epidemiol Community Health. 2021;76(3):301–8. PubMed

Sanson AV, Nicholson JM, Ungerer JA, Zubrick SR, Wilson K, editors. Introducing the longitudinal study of Australian children—LSAC discussion paper no. 12002.

Corey J, Gallagher J, Davis EA, Marquardt M, editors. The times of their lives: collecting time use data from children in the longitudinal study of Australian children (LSAC); 2014.

Templ M, Hron K, Filzmoser P. robCompositions: an R-package for robust statistical analysis of compositional data. In: Compositional data analysis. Boca Raton: CRC Press; 2011. p. 341–55.

Varni JW, Burwinkle TM, Seid M. The PedsQL 4.0 as a school population health measure: feasibility, reliability, and validity. Qual Life Res. 2006;15(2):203–15. PubMed

Varni JW. Scaling and scoring of the pediatric quality of life inventory PedsQL. http://www.pedsql.org/PedsQL-Scoring.pdf.

Blakemore T, Strazdins L, Gibbings J. Measuring family socioeconomic position. Aust Soc Policy J. 2009;8:121–68.

Van den Boogaart KG, Tolosana-Delgado R. “Compositions”: a unified R package to analyze compositional data. Comput Geosci. 2008;34(4):320–38.

Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. shiny: web application framework for R. R package version 1.6.0. https://CRAN.R-project.org/package=shiny.

Dumuid D, Pedišić Ž, Palarea-Albaladejo J, Martín-Fernández JA, Hron K, Olds T. Compositional data analysis in time-use epidemiology: what, why, how. Int J Environ Res Public Health. 2020;17(7):2220. PubMed PMC

Sakia RM. The Box-Cox transformation technique: a review. J R Stat Soc Ser D (Stat). 1992;41(2):169–78.

Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846–57. PubMed

Wu T, Gao X, Chen M, van Dam RM. Long-term effectiveness of diet-plus-exercise interventions vs. diet-only interventions for weight loss: a meta-analysis. Obes Rev. 2009;10(3):313–23. PubMed

Foster-Schubert KE, Alfano CM, Duggan CR, Xiao L, Campbell KL, Kong A, et al. Effect of diet and exercise, alone or combined, on weight and body composition in overweight-to-obese postmenopausal women. Obesity. 2012;20(8):1628–38. PubMed PMC

Loef M, Walach H. The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis. Prev Med. 2012;55(3):163–70. PubMed

Salam RA, Padhani ZA, Das JK, Shaikh AY, Hoodbhoy Z, Jeelani SM, et al. Effects of lifestyle modification interventions to prevent and manage child and adolescent obesity: a systematic review and meta-analysis. Nutrients. 2020;12(8):2208. PubMed PMC

Janssen I, Clarke AE, Carson V, Chaput JP, Giangregorio LM, Kho ME, et al. A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S248–57. PubMed

O’Neil A, Quirk SE, Housden S, Brennan SL, Williams LJ, Pasco JA, et al. Relationship between diet and mental health in children and adolescents: a systematic review. Am J Public Health. 2014;104(10):e31–42. PubMed PMC

Sofi F, Abbate R, Gensini GF, Casini A. Accruing evidence on benefits of adherence to the Mediterranean diet on health: an updated systematic review and meta-analysis. Am J Clin Nutr. 2010;92(5):1189–96. PubMed

Burgess E, Hassmén P, Welvaert M, Pumpa KL. Behavioural treatment strategies improve adherence to lifestyle intervention programmes in adults with obesity: a systematic review and meta-analysis. Clin Obes. 2017;7(2):105–14. PubMed

Lewis LK, Rowlands AV, Gardiner PA, Standage M, English C, Olds T. Small steps: preliminary effectiveness and feasibility of an incremental goal-setting intervention to reduce sitting time in older adults. Maturitas. 2016;85:64–70. PubMed